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REMOTE SENSING AND GIS APPLICATION INAGRO-ECOLOGICAL ZONING

N.R. PatelAgriculture and Soils DivisionIndian Institute of Remote Sensing, Dehra Dun

Abstract : Sustainable agricultural development requires a systematic effort towardsthe planning of land use activities in the most appropriate way, apart from severalother institutional and policy programme initiatives. Agro-ecological zoning (AEZ)is one of the most important approaches for agricultural developmental planningbecause survival and failure of particular land use or farming system in a given regionheavily relies on careful assessment of agro-climatic resources. This approach is usedto categorize agro-climatically uniform geographical areas for agriculturaldevelopmental planning and other interventions. Modern tools such as satelliteremote sensing and Geographical Information System (GIS) have been providingnewer dimensions to effectively monitor and manage land resources in an integratedmanner for agro-ecological characterization. The application of AEZ is limited bylack of geospatial data, particularly in mountainous areas. This paper tries todemonstrate incorporation of new tools to extend applicability of AEZ in

mountainous areas like Kumaon Himalayas, India.

INTRODUCTION

Sustainable agricultural development requires a systematic effort towards theplanning of land use activities in the most appropriate way, apart from

several other institutional and policy programmme initiatives. Agro-ecologicalzoning (AEZ) is one of the most important bases for agricultural developmentalplanning because survival and failure of particular land use or farming systemin a given region heavily relies on careful assessment of agro-climatic resources.A practical zoning approach thus arises because climate represented by thermaland moisture regimes forms small geographic areas, resulting in a variablemosaic of specialized areas, capable of supporting varied land use systems (Troll,1965). The approach is used to categorize agroclimatically uniform

Satellite Remote Sensing and GIS Applications in Agricultural Meteorologypp. 213-233

214 Remote Sensing and GIS Application in Agro-Ecological Zoning

geographical areas for agricultural developmental planning and otherinterventions. A framework of agro-ecological zoning describing concepts,methods and procedures was conceptualized for the first time by FAO (1976).Agro-ecological zoning refers to the division of an area of land into landresource mapping units, having unique combination of landform, soil andclimatic characteristics and or land cover having a specific range of potentialsand constraints for land use (FAO, 1996). The particular parameters used inthe definition focus attention on the climatic and edaphic requirements ofcrop and on the management systems under which the crops are grown. Eachzone has a similar combination of constraints and potentials for land use andserves as a focus for the targeting of recommendations designed to improvethe existing land use situation, either through increasing production or bylimiting land degradation. The addition of further layers of information onsuch factors as land tenure, land availability, nutritional requirement of humanand livestock populations, infrastructure and costs and prices, has enabled thedevelopment of more advanced applications in natural resource analysis andland use planning. AEZ can be regarded as a set of core applications, leadingto an assessment of land suitability and potential productivity. An output ofAEZ studies includes maps showing agro-ecological zones and land suitability,quantitative estimates on potential crop yields and production. Suchinformation provides the basis for advanced applications such as landdegradation assessment, livestock productivity modeling, population supportcapacity assessment and land use optimization modeling.

IMPORTANCE OF AEZ IN SUSTAINABLE AGRICULTURALDEVELOPMENT PLANNING

The ability of the world’s natural resources to provide for the needs of itsgrowing population is a fundamental issue for the international community.World’s population is increasing rapidly and at the same time, essential naturalresources, such as land and water, are declining both in quantity and qualitydue to such factors as competition with industrial and urban demands. Thebasic problem is that limits to the productive capacity of land resources areset by climate, soil and land forms condition. In this context AEZ can beregarded as a set of applications, leading to an assessment of land suitabilityand potential productivity in terms of climate, soil and land forms condition.

Agro-Ecological Zoning (AEZ) is one of the most important bases ofsustainable agricultural development planning of a region. It is applicable inmicro or local level planning mainly for rainfed agriculture. It assesses basicallythe yield potentialities of various crop conditions; evolves future plan of action

N.R. Patel 215

involving crop diversification; determines suitability of different crops foroptimizing land use, disseminates research results and agro-technology. As aresult sustainable agricultural development planning is increasingly being basedon agro-ecological zones. In this process agro climate zoning has become verypopular (Verma and Partap, 1989). The initial focus of the FAO agro-ecologicalzoning system was to assess the suitability of different types of land use forselected land uses. It is an important starting point for selected land useplanning with an overview of the whole region. It diagnoses the presentsituation with regard to farming and land use by categorizing, describing andanalyzing, farming systems components.

DEFINITIONS

Agro-Ecological Zoning (AEZ) refers to the division of an area of land intosmaller units, which have similar characteristics related to land suitabilitypotential production and environmental impact.

Agro-Ecological Zone is a land resource mapping unit, having a uniquecombination of land form, soil and climatic characteristics and/or land coverhaving a specific range of potentials and constraints for land use (FAO, 1996).

Agro-Ecological Cell (AEC) is defined by a unique combination of land form,soil and climatic characteristics

TRADITIONAL APPROACH

Several attempts have been made to classify the land area into climaticregions. Many of the earlier efforts to delineate agro-climates used manualoverlay of isolines representing either potential evapotranspiration ortemperature or their combinations and superimposed on soil resource maps.Carter (1954) divided India into six climatic regions, ranging from arid toper humid, based on the criteria of Thornthwaite system of climateclassification. Sehgal et al. (1987) prepared a computerized bio-climatic mapof NW India, based on the criteria of dry month. Krishnan (1988) delineated40 soil climatic zones based on major soil types and moisture index. Murthyand Pandey (1978) brought out a 8 agro-ecological region map of India onthe basis of physiography, climate, soils and agricultural regions. The approachdepicts a good beginning of agro-ecological zoning in the country, but it suffersfrom several limitations due to over generalizations such as grouping togetherthe areas having different physiography, temperature and soil in zone.


Subramanian (1983) based on the data of 160 meteorological stations inthe country and using the concept of moisture adequacy index, delineated29 agro-ecological zones with the possible 36 combinations of IMA anddominated soil groups as per FAO/UNESCO Soil Map (1974).The planningcommission, as a result of mid-term appraisal of the planning targets of VIIPlan (1985-1990), divided the country into 15 broad agro-climatic zonesbased on physiography and climate (Sehgal et al., 1992).

NEW TOOLS FOR AEZ

Modern tools such as satellite remote sensing and GIS have been providingnewer dimensions to effectively monitor and manage natural resources. It hasbeen well conceived that remote sensing and GIS have great role to play inagro-ecological zoning for sustainable development due to multi-stage characterof the comprehensive approach to agro-ecological zoning (Pratap et al., 1992).Several approaches of AEZ in past involved manual integration of agro-climaticand other natural resource data (Mavi, 1984; Venkateswaralu et al., 1996).As a result, large amount of agro-ecological data could not be handled easilyand aggregation was required at an early stage in the analysis. This led to lossof information on spatial variability. On the other hand, GIS technology isvery useful for automated logical integration of bio-climate, terrain and soilresource inventory information (Patel et al., 2000). The system is capable ofcontaining all data required to solve resource management problems.Topographic maps, land resource map and contour map having physiographic,geographic and bio-climatic information forms primary input for GIS for agro-ecological zoning activities. The system also facilitates the enlargement of aparticular geographic pocket to render more details on retrieval. After collectingthe basic data on zonal resource information, the data can be manipulated tocreate relevant profiles of applied use that can be retrieved on demand. A zonaldatabase can also be integrated with non-geographic information such associoeconomic data, which is relevant for making decision on developmentpriority interventions about the sustainable management of zonal resources.Remote sensing provides digital or hard copy data base information on naturalresources. This information can be stored and retrieved as and when requiredand also data can be classified and aggregated for any number of planningexercises. This AEZ concept involves the representation of land in layers ofspatial information and combination of layers of spatial information usinggeographic information system (GIS).

N.R. Patel 217

STRUCTURE OF AEZ

The structure of AEZ includes the comprehensive framework for theappraisal and planning of land resources (Figure 1). The nature of analysis,which involves the combination of layers of spatial information to define zones,lends itself to application of a GIS. The major requirements of computerizedGIS for an activity like agro-ecological zoning and zonal resource informationof mountain areas are topographic maps, land resource maps and contour maps.These maps, containing physiographic, geographic, and bio-climaticinformation form primary inputs. Various outputs are generated in bothtabular and map forms. Till date, good progress has been made in developingGIS based tools for land resources planning, management and monitoring atdifferent scales.

ELEMENTS OF AEZ

The essential elements of the core applications of AEZ include :

Land resource inventory, comprising

Analysis of length of growing period or moisture availability index

Defining thermal zones

Compilation of climatic resource inventory

Compilation of soil and landform resource inventory

Compilation of present land use inventory

Combination of above to make land resource inventory based on agro-ecological zones or agro-ecological cells.

Inventory of land utilization types and crop requirements

Land suitability evaluation, including

• Potential productivity computations

• Matching of constraints and requirements


A CASE STUDY ON AGRO-ECOLOGICAL ZONING IN MOUNTAINECOSYSTEM

Sustainable development of mountain regions is a challenging task becausethe areas have highly diverse and fragile ecosystems. One of the most strikingcharacteristics of mountains is their spatial variability. This makes the planningof the use of natural resources in the mountains more complex than any otherarea. In view of this, the present study was conducted in part of KumaonHimalayas (latitudes 28o45’ to 30o00’N, longitude 78o45’ to 80o15’) todemonstrate the use of remote sensing and GIS as a tools for agro-ecologicalzoning with mountain perspective (Patel et al., 2002). The methodology usedin this study is outlined in the flow diagram in Figure 2. This methodologyis further described as following sub heads.

Climate

Long term (approximately 10 years) monthly maximum and minimumtemperatures were collected from six meteorological stations falling in the

Figure 1: Structure of land resource

Data base for definition of Agro-Ecological Zoning

Land Resource Overlay

Thermal Zones

Rainfall

Moisture Zones

Agro-ecological cellsInformation in computerised GIS form for each cell. Thermal regimes (3 zones) soil moisture (8zones) Rainfall (5 zones) Soil Texture (3 classess) Slope (4 classes) Physiography (3 types) Elevation (5 zones) Soil unit (47 types) Landuse (10 classes)

Physiography

Elevation Zones

Biomass

Slope

Soil Texture

Land use/Land cover

N.R. Patel 219

western Himalayas. However, monthly rainfall data for eight years werecollected from eleven rain gauge stations within the study area. The detailsabout location of meteorological and rain gauge stations are shown in Table1. Digital elevation data with one-kilometer grid size was taken from GlobalDigital Elevation Model (USGS GDEM) and geometrically registered in UTMprojection. Validation and accuracy check for digital elevation data derivedfrom Global DEM was done with spot height observation from Survey of Indiatoposheets on 1:250000 scale. A close agreement was observed between spotheight observations and Global DEM based elevation data (R2 = 0.98). Longterm monthly and annual averages of mean temperatures of six meteorologicalstations were regressed against corresponding elevation using MICROSTAstatistical software (Table 2). A good agreement was also observed betweenannual mean temperature and elevation (annual mean temperature = 24.443- 0.0045 × elevation, R2 = 0.97). Similarly long term average annual rainfallrecorded at different rain gauge stations were regressed against elevation fordeveloping rainfall – elevation relationship (annual rainfall = 515.1 + 0.3843× elevation, R2 = 0.75) representing the region under study. The empiricalrelations thus developed were used to utilize inherent spatial quality of digitalelevation model in GIS environment for depicting spatial variation in normal

Figure 2 : Flow diagram of the method for agro-ecological zoning

Soil Maps:PhysiographyTextureSlopeDepth

Bioclimate &Moistureregines

BiomassComposite land

use

Landuse/landcover

(Kharif/Rabi)

TemporalIRS WIFS

Data

NOAAAVHRR

NDVImaxTmax, Tmin

RainfallPET maps

Suggested SuitableLanduse

Global 1.0 kmDEM

ClimateInterpolar

Tmax, TminRainfall

(Weather station)

Agro-Ecological Zoens


monthly and annual mean temperatures as well as annual rainfall conditionover Kumaon region. These monthly spatial distribution of mean temperatureswere used for computation of spatial potential evapotranspiration (PET) basedon Thornthwaite (1948) as:

PET = 1.6 [ (10 Tijk/Ijk)ajk ]

where,

Dec

Ijk = ∑ (Tijk/5)1.514

i =Jan

T = Mean air temperature (°C)

i = Month of a year (i = Jan, Feb, ……Dec)

j = Pixel value of i th row

k = Pixel value of j th column

ajk =67.5*10-8(Ijk)3-7.71*10-5*(Ijk)

2+0.01792( Ijk)+0.4923

These monthly potential evapotranspiration were summed over twelvemonths in a year to obtain spatial distribution of annual PET for use incomputation of moisture index (MI). The moisture index with positive andnegative values indicates moist or dry climate and seasonal variation of effectivemoisture. The revised moisture index of Thornwaite and Mather (1955)method based on annual rainfall and potential evapotranspiration wascalculated as,

MI =[( P – PET)/PET] * 100

where,

MI = Moisture Index

P = Rainfall (mm)

PET = Potential Evapotranspiration (mm)

This information on moisture index is vital to congenial biotic environmentand has been used to classify the climatic types. To arrive at homogeneouszones, the limit of moisture index of the various climatic types was scaled asshown in Table 3.

N.R. Patel 221

Tab

le 1

. D

ata

for

field

s an

d lo

cati

on o

f m

eteo

rolo

gica

l an

d ra

in g

auge

sta

tion

s

No.

Met

/rai

n ga

uge

Dat

a fie

lds

Peri

od- L

atitu

deEl

evat

ion

Sour

cest

atio

nM

onth

ly(y

ears

)- L

ongi

tude

(met

er)

1.M

ukte

swar

Rai

nfal

l, Te

mpe

ratu

re10

29 o 2

7’ 2

8.7”

N22

75In

stitu

te T

empe

rate

79 o 3

9’ 2

7.2”

EH

ortic

ultu

re S

tatio

n

2.H

awal

bagh

Rai

nfal

l, Te

mpe

ratu

re10

29o 3

6’ 0

0.0”

N12

50M

et. S

tatio

n79

o 40’

00.

0” E

3.A

lmor

aR

ainf

all,

Tem

pera

ture

1029

o 3

5’ 2

2.4”

N15

28V

ivek

anan

d IC

AR

79 o 3

8’ 4

2.2”

EIn

stitu

te

4.Pa

ntna

gar

Rai

nfal

l, Te

mpe

ratu

re10

29 o 0

2’ 2

7.2”

N23

2A

gric

ultu

re U

nive

rsity

,79

o 2

4’ 3

0.5”

EPa

ntna

gar

5.N

aini

tal

Rai

nfal

l, Te

mpe

ratu

re10

29 o 2

1’ 3

0.7”

N19

45A

stro

nom

y St

ate

79 o 2

7’ 2

6.3”

EO

bser

vato

ry, N

aini

tal

6.D

ehra

Dun

Rai

nfal

l,Tem

pera

ture

1030

o 19’

00.

0” N

682

Met

. Sta

tion

78o 0

2’ 0

0.0”

E

7.M

usso

orie

Rai

nfal

l, Te

mpe

ratu

re10

30o 2

7’ 0

0.0”

N20

24M

et. S

tatio

n, C

SWC

RII

78o 0

5’ 0

0.0”

ED

ehra

Dun

8.C

hauk

hutia

Rai

nfal

l8

29 o 5

2’ 3

2.1”

N97

6Fo

rest

Con

serv

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n79

o 2

2’ 1

5.6’

ED

epar

tmen

t, R

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het

9.K

edar

Rai

nfal

l8

29 o 4

7’ 0

7.8’

N95

8Fo

rest

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serv

atio

n79

o 1

5’ 2

6.8”

ED

epar

tmen

t, R

anik

het


10.

Nau

laR

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all

829

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4’ 1

3.5”

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rest

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serv

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n79

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5 11

.8”

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t, R

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11.

Tam

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829

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0’ 4

9.0”

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rest

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serv

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n79

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2’ 0

5.4”

ED

epar

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t, R

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12.

Saun

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all

829

o 37’

47.

9” N

1599

Fore

st C

onse

rvat

ion

79o 2

2’ 0

5.8”

ED

epar

tmen

t, R

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het

13.

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all

829

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8’ 5

1.1”

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n79

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0.6”

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14.

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all

829

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7’ 1

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4.5”

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epar

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anik

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No.

Met

/rai

n ga

uge

Dat

a fie

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Peri

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evat

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Sour

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nM

onth

ly(y

ears

)- L

ongi

tude

(met

er)

N.R. Patel 223

Table 3. Scaling of moisture index into different agro-climate types

Climatic type Moisture Index Symbol

Semi arid -66.7 to – 33.3 D

Dry sub-humid - 33.3 to 0 C1

Moist sub-humid 0 to 20 C2

Humid (4 classes) 20 to 100 B1, B2, B3 and B4

Per-humid > 100 A

Vegetation Biomass variability

Using NDVI to estimate standing green biomass proved to be a reliablesource of biomass data. The existing functional relationship between monthlycutting of green dry matter (DM) and maximum normalized differencevegetation index (NDVI) derived from NOAA AVHRR (Advanced Very HighResolution Radiometer) was used. Monthly NDVI images of NOAA AVHRRduring year 1999 were used to derive the maximum NDVI in a year forestimating biomass or dry matter (DM) as per equation.

Table 2. Mean Temperature v/s Elevation Relationship

Month Regression Equation R2

January y = - 0.0031 x + 14.106 0.89

February y = - 0.004 x + 17.012 0.95

March y = - 0.0047 x + 22.122 0.97

April y = - 0.0051 x + 27.95 0.96

May y = - 0.0057 x + 31.485 0.98

June y = - 0.0059 x + 31.925 0.96

July y = - 0.0058 x + 30.642 0.99

August y = - 0.0054 x + 29.755 0.98

September y = - 0.0053 x+ 28.855 0.99

October y = - 0.0044 x + 25.133 0.95

November y = - 0.003 x + 19.284 0.89

December y = - 0.0018 x + 15.045 079


DM = (1.615*NDVImax)1.318 ; R2 = 0.90

DM = Dry matter

NDVImax = Maximum NDVI in a year.

Land use/land cover

Land use/land cover was derived from coarse resolution IRS WiFS satellitedata. Ground truth was collected through integrated use of previous year IRSLISS III hard copy (1:250,000 scale) satellite data, Survey of India (SOI)toposheets and handheld Global Positioning System (GPS). Combination ofsatellite data acquired during kharif (September and October, 2001) and rabi(January and March, 2002) seasons were digitally classified to land use/landcover information classes for kharif and rabi, respectively using MXL classifier.Agricultural land use classes in rabi season were refined with respect to landuse information in kharif season and crop calendar of major crops cultivatedin the region. Finally, classified land use /land cover information of kharif andrabi seasons were logically integrated on a pixel by pixel basis in raster GISfor deriving cropping system or composite land use/land cover (Fig. 3).

Figure 3: Suggested land use based on AEZs and present land use

Barren/ScrubDeciduous ForestDense ForestFallow - FallowFallow - SugarcaneFallow - WheatForest PlantationLakeModerately Dense Forest

Open ForestPaddy - FallowPaddy -SugarcanePaddy - WheatRiverSettlementSugarcaneSugarcane - FallowSugarcane - Wheat

AfforestationAgro HorticultureDouble crop agricultureForestForest PlantationLakeRiver SettlementSocial Forestry

Suggested Land UseComposite Land Use

N

N.R. Patel 225

Soils

Forty-six soil mapping unit along with their characteristics such as depth,texture, slope, erosion status, and drainage condition for the study area wereobtained from soil map (1:250000 scale) prepared by National Bureau of SoilSurvey and Land Use Planning, India. These spatial and non spatial data onsoils were converted into digital soil resource databases for AEZ. In generalterms, coarse loamy and skeletal soils are in side slope and top of LesserHimalayas and piedmont plain while fine textured soils mainly found inalluvial plain and fluvial valleys. The soils are mostly moderately deep to deep,however, approximately 25 % of the area having shallow soils were mainlyfound in Lesser Himalayas and piedmont plain (Fig. 4).

Figure 4: Characteristics of agro-edaphic and agro-climatic zones

All the land resource databases and characteristics (Fig. 1) described aboveas layer or combination of layers of spatial information were integrated oroverlaid to derive different unique agro-ecological cells. These agro-ecologicalcells were further aggregated to arrive at agro-ecological zones and sub zonesbased on their potential to support agriculture and vegetation patterns.

Delineation of agro-climatic zones

Agro-climatic zones are of paramount importance for defining ordelineation of agro-ecological zoning for sustainable use of land resources. Theessential elements in demarcating or defining of an agro-climatic zone arebioclimates based on thermal regimes, moisture regime and biomass variability


(Fig. 5). Thermal regimes indicate to the amount of heat available for plantgrowth and development during the growing period. Moisture regimesrepresent water availability for crop production and hence they are the vitalto classify the agro-climatic zones. Nine different agro-climatic zones weredelineated by GIS aided integration of thermal regimes, moisture regimes andbiomass map layers (Fig. 5).

Figure 5: Input layers for agro-ecological zoning

Delineation of Agro-ecological zones and sub-zones

Thirty agro-ecological sub zones (AEZIa to AEZXIIc) were delineated inthe study area (Table 4) by GIS aided integration of nine agro-climatic andeight agro-edaphic zones (Fig. 6). Results revealed that the majority of areafalls in XIa whereas minimum area is occupied by Via (Patel et al., 2002).The other larger sub-zones are Ib and VIIIa, respectively. AEZIc and AEZIIcare the most potential zones for intensive cropping with possibility of twoand three crops such as paddy – wheat or sugarcane – wheat (Table-4). Furthersub-ecological zones are reclassified by GIS aided integration in which sub-zones are clubbed into main agro-ecological zones. Therefore, twelve agro-ecological zones (AEZI to AEZXII) are identified in the study area. The spatial

N.R. Patel 227

Tab

le 4

. Sp

atia

l ex

tent

of

Agr

o-E

colo

gica

l Su

b Z

ones

Sl.N

o.A

EZA

E S

ub Z

one

Des

crip

tion

Are

a (h

a)(%

)

1.I

aE

quat

oria

l, se

mia

rid

very

gen

tly sl

opin

g al

luvi

al p

lain

with

loam

y to

fine

7448

7.48

6.50

loam

y so

ils a

nd lo

w b

iom

ass

2.I

bE

quat

oria

l, se

mia

rid

very

gen

tly sl

opin

g al

luvi

al p

lain

with

loam

y to

fine

1079

97.1

9.42

loam

y so

ils a

nd m

ediu

m b

iom

ass

3.I

cE

quat

oria

l, se

mia

rid

very

gen

tly sl

opin

g al

luvi

al p

lain

with

loam

y to

fine

1011

1.92

0.88

loam

y so

ils a

nd h

igh

biom

ass

4.II

aE

quat

oria

l, se

mia

rid

very

gen

tly sl

opin

g in

allu

vial

pla

in w

ith c

oars

e lo

amy

8001

5.28

6.98

soils

and

low

bio

mas

s

5.II

bE

quat

oria

l, se

mia

rid

very

gen

tly sl

opin

g in

allu

vial

pla

in w

ith c

oars

e lo

amy

6413

5.22

5.59

soils

and

med

ium

bio

mas

s

6.II

cE

quat

oria

l, se

mia

rid

very

gen

tly sl

opin

g in

allu

vial

pla

in w

ith c

oars

e lo

amy

2371

.58

0.21

soils

and

hig

h bi

omas

s

7.II

Ia

Equ

ator

ial,

sem

iari

d ge

ntly

slop

ing

valle

y fil

ls w

ith c

oars

e lo

amy

soils

and

4633

5.98

4.04

low

bio

mas

s

8.II

Ib

Equ

ator

ial,

sem

iari

d ge

ntly

slop

ing

valle

y fil

ls w

ith c

oars

e lo

amy

soils

and

2475

8.47

2.16

med

ium

bio

mas

s

9.II

Ic

Equ

ator

ial,

sem

iari

d ge

ntly

slop

ing

valle

y fil

ls w

ith c

oars

e lo

amy

soils

and

77.7

60.

01hi

gh b

iom

ass

10.

IVa

Sub

equa

tori

al, S

ub h

umid

to h

umid

gen

tly sl

opin

g va

lley

fills

with

coa

rse

3453

1.08

3.02

loam

y so

ils a

nd lo

w b

iom

ass


11.

IVb

Sub

equa

tori

al, S

ub h

umid

to h

umid

gen

tly sl

opin

g va

lley

fills

with

coa

rse

1163

1.71

1.01

loam

y so

ils a

nd m

ediu

m b

iom

ass

12.

Va

Equ

ator

ial,

sem

iari

d ge

ntly

slop

ing

pied

mon

t pla

in w

ith c

oars

e lo

amy

soil

4215

1.25

3.68

and

low

bio

mas

s

13.

Vb

Equ

ator

ial,

sem

iari

d ge

ntly

slop

ing

pied

mon

t pla

in w

ith c

oars

e lo

amy

soil

3426

6.01

2.99

and

med

ium

bio

mas

s

14.

Vc

Equ

ator

ial,

sem

iari

d ge

ntly

slop

ing

pied

mon

t pla

in w

ith c

oars

e lo

amy

soil

968.

430.

08an

d hi

gh b

iom

ass

15.

VI

aSu

b eq

uato

rial

, Sub

hum

id to

hum

id g

ently

slop

ing

pied

mon

t pla

in w

ith7.

070.

00co

arse

loam

y so

ils a

nd lo

w b

iom

ass

16.

VI

bSu

b eq

uato

rial

, Sub

hum

id to

hum

id g

ently

slop

ing

pied

mon

t pla

in w

ith11

80.4

81.

10co

arse

loam

y so

ils

17.

VII

aW

arm

tem

pera

te, p

er h

umid

mod

erat

ely

stee

p to

stee

p slo

ping

pie

dmon

t54

44.5

90.

57pl

ain

with

coa

rse

loam

y so

ils a

nd lo

w b

iom

ass

18.

VII

bW

arm

tem

pera

te, p

er h

umid

mod

erat

ely

stee

p to

stee

p slo

ping

pie

dmon

t69

76.9

0.61

plai

n w

ith c

oars

e lo

amy

soils

19.

VII

Ia

Sub

equa

tori

al, S

ub h

umid

to h

umid

mod

erat

e st

eep

to st

eep

slopi

ng H

ills

1231

03.1

10.7

5w

ith lo

am to

fine

loam

soils

and

low

bio

mas

s

20.

VII

Ib

Sub

equa

tori

al, S

ub h

umid

to h

umid

mod

erat

e st

eep

to st

eep

slopi

ng H

ills

6566

2.08

5.72

with

loam

to fi

ne lo

am so

ils

Sl.N

o.A

EZA

E S

ub Z

one

Des

crip

tion

Are

a (h

a)(%

)

N.R. Patel 229

21.

IXa

Equ

ator

ial,

sem

iari

d m

oder

ate

stee

p to

stee

p slo

ping

in H

ills w

ith lo

am to

3535

4.6

3.08

fine

loam

y so

ils a

nd lo

w b

iom

ass

22.

IXb

Equ

ator

ial,

sem

iari

d m

oder

ate

stee

p to

stee

p slo

ping

in H

ills w

ith lo

am to

1556

1.97

2.67

fine

loam

y so

ils a

nd m

ediu

m b

iom

ass

23.

IXc

Equ

ator

ial,

sem

iari

d m

oder

ate

stee

p to

stee

p slo

ping

in H

ills w

ith lo

am to

498.

35.0

5fin

e lo

amy

soils

and

hig

h bi

omas

s

24.

Xa

War

m te

mpe

rate

, per

hum

id m

oder

atel

y st

eep

to st

eep

slopi

ng H

ills w

ith22

86.7

60.

20co

arse

loam

y sk

elet

al so

ils a

nd lo

w b

iom

ass

25.

Xb

War

m te

mpe

rate

, per

hum

id m

oder

atel

y st

eep

to st

eep

slopi

ng H

ills w

ith39

90.3

30.

34co

arse

loam

y sk

elet

al so

ils a

nd m

ediu

m b

iom

ass

26.

XI

aSu

b eq

uato

rial

, Sub

hum

id to

hum

id m

oder

ate

stee

p to

stee

p slo

ping

Hill

s18

2201

.915

.88

with

loam

y sk

elet

al so

ils a

nd lo

w b

iom

ass

27.

XI

bSu

b eq

uato

rial

, Sub

hum

id to

hum

id m

oder

ate

stee

p to

stee

p slo

ping

Hill

s10

6272

9.27

with

loam

y sk

elet

al so

ils a

nd m

ediu

m b

iom

ass

28.

XII

aE

quat

oria

l, se

mia

rid

mod

erat

e st

eep

to st

eep

slopi

ng H

ills w

ith l

oam

y29

40.6

20.

26sk

elet

al so

ils a

nd lo

w b

iom

ass

29.

XII

bE

quat

oria

l, se

mia

rid

mod

erat

e st

eep

to st

eep

slopi

ng H

ills w

ith l

oam

y43

830.

093.

82sk

elet

al so

ils a

nd m

ediu

m b

iom

ass

30.

XII

cE

quat

oria

l, se

mia

rid

mod

erat

e st

eep

to st

eep

slopi

ng H

ills w

ith l

oam

y35

3.44

0.03

skel

etal

soils

and

hig

h bi

omas

s

Sl.N

o.A

EZA

E S

ub Z

one

Des

crip

tion

Are

a (h

a)(%

)


Tab

le 5

. Sp

atia

l ex

tent

of

Agr

o-E

colo

gica

l Z

ones

Sl.N

o.A

EZD

escr

iptio

nA

rea

(ha)

(%)

1.I

Equ

ator

ial,

sem

iari

d ve

ry g

ently

slop

ing

in a

lluvi

al p

lain

with

loam

y to

fine

loam

y so

ils19

2596

.516

.8

2.II

Equ

ator

ial s

emia

rid

very

gen

tly sl

opin

g in

allu

vial

pla

in w

ith c

oars

e lo

amy

soi

ls14

6522

.112

.78

3.II

IE

quat

oria

l, se

mia

rid

gent

ly sl

opin

g in

val

ley

fills

with

coa

rse

loam

y so

ils71

172.

216.

21

4.IV

Sub

equa

tori

al, S

ub h

umid

to h

umid

gen

tly sl

opin

g va

lley

fills

with

coa

rse

loam

y so

ils48

42.1

34.

03

5.V

Equ

ator

ial,

sem

iari

d ge

ntly

slop

ing

pied

mon

t pla

in w

ith c

oars

e lo

amy

soil

7738

5.69

6.75

6.V

ISu

b eq

uato

rial

, Sub

hum

id to

hum

id g

ently

slop

ing

pied

mon

t pla

in w

ith c

oars

e lo

amy

soils

1187

.55

1.10

7.V

IIW

arm

tem

pera

te, p

er h

umid

mod

erat

ely

stee

p to

stee

p slo

ping

pie

dmon

t pla

in w

ith c

oars

elo

amy

soils

1242

1.49

1.18

8.V

III

Sub

equa

tori

al, S

ub h

umid

to h

umid

mod

erat

e st

eep

to st

eep

slopi

ng H

ills w

ith lo

amy

tofin

e lo

amy

soils

1887

65.2

16.4

7

9.IX

Equ

ator

ial,

sem

iari

d m

oder

ate

stee

p to

stee

p slo

ping

in H

ills w

ith lo

am to

fine

loam

y so

ils51

414.

926.

80

10.

XW

arm

tem

pera

te, p

er-h

umid

mod

erat

ely

stee

p to

stee

p slo

ping

Hill

s with

coa

rse

loam

ysk

elet

al so

ils62

77.0

90.

54

11.

XI

Sub

equa

tori

al, S

ub h

umid

to h

umid

mod

erat

e st

eep

to st

eep

slopi

ng H

ills w

ith lo

amy

skel

etal

soils

2884

74.2

25.1

5

12.

XII

Equ

ator

ial,

sem

iari

d m

oder

ate

stee

p to

stee

p slo

ping

Hill

s with

loa

my

skel

etal

soils

4712

4.15

4.11

N.R. Patel 231

distribution of these zones is presented in Figure 6. The area covered rangesfrom 0.54 % in AEZ X to 25.15 % in AEZ XI. The whole hilly portion fallsin AEZ VIII whereas lower alluvial plain in AEZ I and AEZ II (Table-5). Asuitable land use (Fig. 3) plan for sustainable use of land resource for theKumaon region was suggested based on characteristics of AEZs and existingland use pattern.

Figure 6. Agro-ecological zones

CONCLUSIONS

A Remote Sensing and Geographical Information System based approachto delineate Agro-Ecologically homogeneous geographical areas was developedusing soil resources, temperature, rainfall, moisture, and biomass as inputlayers. Based on delineated Agro-ecological Zones, suitable land use weresuggested in Kumaon region of Almora, Nainital, Champawat and UdhamSingh Nagar districts.

Following conclusions were drawn from the study,

1. Strong negative relations between temperatures and elevation (i.e. lapserate) would provide basis for estimating spatial variations in temperature,particularly in the mountain ecosystems.


2. Spatial distribution of PET and Moisture index would help in definingmicro-Environment more accurately in the mountains environment.

3. Integration of bio-climate, moisture regimes and regional vegetationbiomass in GIS environment could provide a more dynamic way ofcharacterizing homogeneous agro-climatic zones for identifying biophysicaland climate characteristics to agricultural productivity.

REFERENCES

Carter, D.B. 1954. Climates of Africa and according to Thornthwaite (1948) Classification.John Hopkins University Publication (in) Climatology, 7(14).

FAO, 1976. A framework for land evaluation. Food and Agricultural Organisation, SoilsBulletin 32, Rome, Italy.

FAO, 1996. Guidelines: Agro-ecological zoning. Food and Agricultural Organisation, SoilsBulletin, Rome, Italy.

Krishnan, A. 1988. Delineation of soil climatic zones of India and its practical applicationsin Agriculture. Fertilizer News, 33 (4).

Mavi, H.S. 1984. Introduction to Agrometeorology, IInd edition. Raju Primlani for Oxford& IBH Publishers Co. Pvt. Ltd.

Murthy, R.S. and Pandey, S. 1978. Delineation of agro-ecological regions of India. Paperpresented in Commission V, 11th Congress of ISSS, Edmonton, Canada, June 19-27.

Pariyar, M.P. and Singh, G. 1994. GIS based model for agro-ecological zoning: a case studyof Chitwan district, Nepal. Proc. 15th Asian Conference Rem. Sens., Bangalore, India.Vol. 1 : A4-1 to A4-6.

Pratap, T., Pradhan, P., Lotta, P.K., Mya, S., Karim and Nakarmi, G. 1992. GeographicInformation Systems and Technology application in Agro-ecological zonation ofmountain agriculture. Eds. N.S. Jodha, M. Banskota and Tej Pratap, Oxford & IBHPublishing Co. Pvt. Ltd., New Delhi.

Patel, N.R., Mandal, U.K. and Pande, L.M. 2000. Agro-ecological zoning system. A RemoteSensing and GIS Perspective. Journal of Agrometeorology, 2 (1) : 1-13.

Patel, N.R., Endang, P., Suresh Kumar and Pande, L.M. 2002. Agro-ecological zoning usingremote sensing and GIS – A case study in part of Kumaon region. In: Sustainableagriculture development, Proce. of 2nd International Conference on SustainableAgriculture, Water Resources Development and Earth Care Policies, 18-20 December,2002, New Delhi (In Press).

N.R. Patel 233

Sehgal, J.L., Vernemmen, C. and Tavernier, R. 1987. Agro-climatic Environments andMoisture Regimes in NW India - Their Applications in Soils and Crop Growth. ResearchBull. NBSS Publ. 17, Nagpur, India, NBSS & LUP.

Sehgal, J.L., Mandal, D.K., Mandal, C. and Vadivelu, S. 1992. Agro-ecological Region ofIndia. NBSS & LUP (ICAR) Publication 24, Nagpur.

Subramanian, A.R. 1983. Agro-ecological Zones of India. Arch. Met. Geoph. Biocl. Ser. B32;329-333.

Thornthwaite, C.E. and Mathew, J.R. 1955. The Water Balance. Publications in Climatology,Vol.8, No.1. Drexel Institute of Technology, Laboratory of Climatology, Centerton, N.J.pp. 104.

Thornthwaite, C.W. 1948. An Approach towards a rational classification of climate.Geographical Review, 38: 55-94.

Troll, C. 1965. Seasonal Climates of the earth. World maps of Climatology. Eds. E. Rodenwaltand H. Jusatz. Berlin : Springer – Verlag, pp. 28.

Venkateshwarlu, J., Ramakrishna, Y.S. and Rao, A.S. 1996. Agro-climatic zones of India.Annals of Arid Zone, 35 (1); 1-8.

Verma and Pratap, 1989. Agro climatic zones. Profiles and Issues. Agro-climatic RegionalPlanning UNIT (ARPU) Working paper, Ahmedabad: Sardar Patel Inst. of Econ. andSoc. Res. 1989.

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